Exgentic/agent-llm-traces
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lenadan
updated a
dataset 2 days ago
Post
1034
Weekend mini project! Since commentary on AI is inherently interdisciplinary, we connected the observations in the Pope's encyclical with decades of scholarship in Responsible AI and Ethics research and created an interactive space with these annotations!
Work with @IJ-Reynolds , @yjernite , and @meg
Lots to unpack. We started with 105 annotations. Please submit pull requests for more that we may have missed!
society-ethics/annotated-encyclical
Work with @IJ-Reynolds , @yjernite , and @meg
Lots to unpack. We started with 105 annotations. Please submit pull requests for more that we may have missed!
society-ethics/annotated-encyclical
Asaf-Yehudai
submitted a
paper to Daily Papers 13 days ago
Elron
updated a
Space 20 days ago
Elron
published a
Space 20 days ago
evijit
submitted a
paper to Daily Papers 29 days ago
lenadan
published a
dataset about 1 month ago
Elron
authored 7
papers 3 months ago
AlephBERT:A Hebrew Large Pre-Trained Language Model to Start-off your Hebrew NLP Application With
Paper • 2104.04052 • Published
Efficient Benchmarking (of Language Models)
Paper • 2308.11696 • Published
Quality Controlled Paraphrase Generation
Paper • 2203.10940 • Published
Lexical Generalization Improves with Larger Models and Longer Training
Paper • 2210.12673 • Published
Benchmark Agreement Testing Done Right: A Guide for LLM Benchmark Evaluation
Paper • 2407.13696 • Published • 5
DOVE: A Large-Scale Multi-Dimensional Predictions Dataset Towards Meaningful LLM Evaluation
Paper • 2503.01622 • Published
General Agent Evaluation
Paper • 2602.22953 • Published • 12
borgr
submitted a
paper to Daily Papers 3 months ago
Post
2879
AI for Scientific Discovery Won't Work Without Fixing How We Collaborate.
My co-author @cgeorgiaw and I just published a paper challenging a core assumption: that the main barriers to AI in science are technical. They're not. They're social.
Key findings:
🚨 The "AI Scientist" myth delays progress: Waiting for AGI devalues human expertise and obscures science's real purpose: cultivating understanding, not just outputs.
📊 Wrong incentives: Datasets have 100x longer impact than models, yet data curation is undervalued.
⚠️ Broken collaboration: Domain scientists want understanding. ML researchers optimize performance. Without shared language, projects fail.
🔍 Fragmentation costs years: Harmonizing just 9 cancer files took 329 hours.
Why this matters: Upstream bottlenecks like efficient PDE solvers could accelerate discovery across multiple sciences. CASP mobilized a community around protein structure, enabling AlphaFold. We need this for dozens of challenges.
Thus, we're launching Hugging Science! A global community addressing these barriers through collaborative challenges, open toolkits, education, and community-owned infrastructure. Please find all the links below!
Paper: AI for Scientific Discovery is a Social Problem (2509.06580)
Join:
hugging-science
Discord: https://discord.com/invite/VYkdEVjJ5J
My co-author @cgeorgiaw and I just published a paper challenging a core assumption: that the main barriers to AI in science are technical. They're not. They're social.
Key findings:
🚨 The "AI Scientist" myth delays progress: Waiting for AGI devalues human expertise and obscures science's real purpose: cultivating understanding, not just outputs.
📊 Wrong incentives: Datasets have 100x longer impact than models, yet data curation is undervalued.
⚠️ Broken collaboration: Domain scientists want understanding. ML researchers optimize performance. Without shared language, projects fail.
🔍 Fragmentation costs years: Harmonizing just 9 cancer files took 329 hours.
Why this matters: Upstream bottlenecks like efficient PDE solvers could accelerate discovery across multiple sciences. CASP mobilized a community around protein structure, enabling AlphaFold. We need this for dozens of challenges.
Thus, we're launching Hugging Science! A global community addressing these barriers through collaborative challenges, open toolkits, education, and community-owned infrastructure. Please find all the links below!
Paper: AI for Scientific Discovery is a Social Problem (2509.06580)
Join:
Discord: https://discord.com/invite/VYkdEVjJ5J
burtenshaw
authored a
paper 8 months ago
burtenshaw
posted an update 9 months ago
Post
8529
Smol course has a distinctive approach to teaching post-training, so I'm posting about how it’s different to other post-training courses, including the llm course that’s already available.
In short, the smol course is just more direct that any of the other course, and intended for semi-pro post trainers.
- It’s a minimal set of instructions on the core parts.
- It’s intended to bootstrap real projects you're working on.
- The material handsover to existing documentation for details
- Likewise, it handsover to the LLM course for basics.
- Assessment is based on a leaderboard, without reading all the material.
To start the smol course, follow here:
smol-course
In short, the smol course is just more direct that any of the other course, and intended for semi-pro post trainers.
- It’s a minimal set of instructions on the core parts.
- It’s intended to bootstrap real projects you're working on.
- The material handsover to existing documentation for details
- Likewise, it handsover to the LLM course for basics.
- Assessment is based on a leaderboard, without reading all the material.
To start the smol course, follow here:
burtenshaw
posted an update 9 months ago
Post
5545
new smol course
If you’re building with or learning about post training AI models right now, we have a new FREE and CERTIFIED course.
🔗 Follow the org to join in smol-course
The course builds on smol course v1 which was the fastest way to learn to train your custom AI models. It now has:
- A leaderboard for students to submit models to
- Certification based on exams and leaderboards
- Prizes based on Leaderboards
- Up to date content on TRL and SmolLM3
- Deep integration with the Hub’s compute for model training and evaluation
We will release chapters every few weeks, so you can follow the org to stay updated.
If you’re building with or learning about post training AI models right now, we have a new FREE and CERTIFIED course.
🔗 Follow the org to join in
The course builds on smol course v1 which was the fastest way to learn to train your custom AI models. It now has:
- A leaderboard for students to submit models to
- Certification based on exams and leaderboards
- Prizes based on Leaderboards
- Up to date content on TRL and SmolLM3
- Deep integration with the Hub’s compute for model training and evaluation
We will release chapters every few weeks, so you can follow the org to stay updated.
- 2 replies
burtenshaw
posted an update 9 months ago
Post
3186
The open source AI community is just made of people who are passionate and care about their work. So we thought it would be cool to share our favourite icons of the community with a fun award.
Winners get free Hugging Face Pro Subscriptions, Merchandise, or compute credits for the hub.
🔗 Follow and nominate here:
community-spotlight
This is a new initiative to recognise and celebrate the incredible work being done by community members. It's all about inspiring more collaboration and innovation in the world of machine learning and AI.
They're highlighting contributors in four key areas:
- model creators: building and sharing innovative and state-of-the-art models.
- educators: sharing knowledge through posts, articles, demos, and events.
- tool builders: creating the libraries, frameworks, and applications that we all use.
- community champions: supporting and mentoring others in forums.
Know someone who deserves recognition? Nominate them by opening a post in the Hugging Face community forum.
Winners get free Hugging Face Pro Subscriptions, Merchandise, or compute credits for the hub.
🔗 Follow and nominate here:
This is a new initiative to recognise and celebrate the incredible work being done by community members. It's all about inspiring more collaboration and innovation in the world of machine learning and AI.
They're highlighting contributors in four key areas:
- model creators: building and sharing innovative and state-of-the-art models.
- educators: sharing knowledge through posts, articles, demos, and events.
- tool builders: creating the libraries, frameworks, and applications that we all use.
- community champions: supporting and mentoring others in forums.
Know someone who deserves recognition? Nominate them by opening a post in the Hugging Face community forum.
- 1 reply